Effects of density, species interactions, and environmental stochasticity on the dynamics of British bird communities

Sandal, Lisa; Grøtan, Vidar; Sæther, Bernt‐Erik; Freckleton, Robert P.; Noble, David G.; Ovaskainen, Otso

doi:10.1002/ecy.3731

Cited by 13 publications

(19 citation statements)

References 87 publications

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“…Moreover, the fact that habitat dissimilarity had a negative relationship with spatiotemporal associations suggests that species sharing habitat preferences tend to be also positively spatiotemporally associated. This supports the view that environmental factors are more important than competition for covariation in bird species abundance at large spatial scales (Dorazio et al, 2015; Sandal et al, 2022).…”

Section: Discussionsupporting

confidence: 84%

“…Please note that the measure for the functional dissimilarity (morphology, diet, morphology & diet) varies according to which measure gave best fit to the data in terms of Akaike information criterion (AIC) (Appendix 7, Table S7.1) and the scale of the x-axes differ. bird species abundance at large spatial scales (Dorazio et al, 2015;Sandal et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Both negative and positive local‐scale interactions among birds can scale up to and be visible at large spatial scales (Belmaker et al, 2015; Gotelli et al, 2010; Heikkinen et al, 2007; Mönkkönen et al, 2017). By contrast, some other studies have found little support for biotic interactions at large spatial scales (Dorazio et al, 2015; König et al, 2021; Royan et al, 2016; Sandal et al, 2022), at least when compared to environmental drivers and averaged over species (Snell Taylor et al, 2020). Hence, while environmental filtering clearly affects bird community assembly, the importance of biotic interactions among bird species at large spatial scales is still unclear.…”

Section: Introductionmentioning

confidence: 95%

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Do large‐scale associations in birds imply biotic interactions or environmental filtering?

Elo

Kajanus

Tolvanen

et al. 2022

Journal of Biogeography

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Aim: There has been a wide interest in the effect of biotic interactions on species' occurrences and abundances at large spatial scales, coupled with a vast development of the statistical methods to study them. Still, evidence for whether the effects of within-trophic-level biotic interactions (e.g. competition and heterospecific attraction) are discernible beyond local scales remains inconsistent. Here, we present a novel hypothesis-testing framework based on joint dynamic species distribution models and functional trait similarity to dissect between environmental filtering and biotic interactions.Location: France and Finland.Taxon: Birds. Methods:We estimated species-to-species associations within a trophic level, independent of the main environmental variables (mean temperature and total precipitation) for common species at large spatial scale with joint dynamic species distribution (a multivariate spatiotemporal delta model) models. We created hypotheses based on species' functionality (morphological and/or diet dissimilarity) and habitat preferences about the sign and strength of the pairwise spatiotemporal associations to estimate the extent to which they result from biotic interactions (competition, heterospecific attraction) and/or environmental filtering.Results: Spatiotemporal associations were mostly positive (80%), followed by random (15%), and only 5% were negative. Where detected, negative spatiotemporal associations in different communities were due to a few species. The relationship between spatiotemporal association and functional dissimilarity among species was negative, which fulfils the predictions of both environmental filtering and heterospecific attraction. Main conclusions:We showed that processes leading to species aggregation (mixture between environmental filtering and heterospecific attraction) seem to dominate assembly rules, and we did not find evidence for competition. Altogether, our hypothesis-testing framework based on joint dynamic species distribution models

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

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Do large‐scale associations in birds imply biotic interactions or environmental filtering?

Elo

Kajanus

Tolvanen

et al. 2022

Journal of Biogeography

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“…Notably, this amount of variation was higher for the dominant replacement component and increased in the second time period. Therefore, stochasticity (appearing possibly more important in driving the replacement of species during the second time period) or other factors not included in the present study (Sandal et al, 2022) contribute to the assembly of native bird communities. Finally, we detected changes in the spatial patterns of predicted native dissimilarity of the two periods probably driven by the changes in the climatic conditions (Wayman et al, 2022;White et al, 2018).…”

Section: Notementioning

confidence: 99%

Replacement drives native β‐diversity of British avifauna, while richness differences shape alien β‐diversity

Lazarina

Sgardelis

Michailidou

et al. 2022

Diversity and Distributions

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“…Accordingly, we propose that four of the five essential elements of a community interaction web can be determined by monitoring (nodes, linkages, interaction signs, and species' abundance) and the fifth element, interaction strengths, can be derived from web topology and dynamical behavior using FIWs (see example below). Such an approach should, in theory, provide interaction strengths that reflect not just pairwise interactions, but the manifestation of pairwise interactions in the context of community dynamics including indirect effects, trait‐mediated interactions, and other higher‐order interactions (Baker et al, 2016; Ives et al, 2003; Kleinhesselink et al, 2022; Sandal et al, 2022).…”

Section: Estimating Species Abundance and Interaction Strengthmentioning

confidence: 99%

Predicting ecological outcomes using fuzzy interaction webs

Pearson

Clark

2023

Ecology

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The past 100 years of empirical research in ecology have generated tremendous knowledge about the component interactions that structure ecological communities. Yet, we still lack the ability to reassemble these puzzle pieces to predict community responses to perturbations, a challenge that grows increasingly urgent given rapid global change. We summarize key advances in community ecology that have set the stage for modeling ecological systems and briefly review the evolution of ecological modeling efforts to identify critical hurdles to progress. We find that while Robert May demonstrated that quantitative models could theoretically predict community interactions nearly 50 years ago, in practice, we still lack the ability to predict ecological outcomes with reasonable accuracy for three reasons: (1) quantitative models require precise data for parameterization (often unavailable) and have restrictive assumptions that are rarely met; (2) estimating interaction strengths for all network components is extremely challenging; and (3) determining which species are essential to include in models is difficult (model structure uncertainty).We propose that fuzzy interaction webs (FIW), borrowed from the social sciences, hold the potential to overcome these modeling shortfalls by integrating quantitative and qualitative data (e.g., categorical data, natural history information, expert opinion) for generating reasonably accurate qualitative predictions sufficient for addressing many ecological questions. We outline recent advances developed for addressing model structure uncertainty, and we present a case study to illustrate how FIWs can be applied for estimating community interaction strengths and predicting complex ecological outcomes in a multitrophic (plants, herbivores, predators), multi-interaction-type (competition, predation, facilitation, omnivory) grassland ecosystem. We argue that incorporating FIWs into ecological modeling could significantly advance empirical and theoretical ecology.

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Effects of density, species interactions, and environmental stochasticity on the dynamics of British bird communities

Cited by 13 publications

References 87 publications

Do large‐scale associations in birds imply biotic interactions or environmental filtering?

Do large‐scale associations in birds imply biotic interactions or environmental filtering?

Replacement drives native β‐diversity of British avifauna, while richness differences shape alien β‐diversity

Predicting ecological outcomes using fuzzy interaction webs

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